Inferring unobserved vector dynamics for dengue forecasting using physics-informed neural networks and mechanistic transmission models
Journal:
bioRxiv
Published Date:
Feb 14, 2026
Abstract
Accurately characterising mosquito infection dynamics is essential for effective dengue prevention and control, yet these dynamics are rarely observable through routine surveillance. Here, we integrate a reduced SI-SIR transmission model with monthly dengue incidence data using physics-informed neural networks (PINNs) to develop a Dengue-Informed Neural Network (DINN). The DINN simultaneously fits reported dengue cases from 15 countries between January 2014 to April 2025 and reconstructs the unobserved time series of infected mosquitoes. Using the inferred mosquito infection dynamics together with five-month-lagged climatic variables, we subsequently train recurrent neural networks (RNN, GRU and LSTM) to forecast mosquito infection levels one month ahead. For each country, the optimal forecasting model is selected based on out-of-sample performance, and model behaviour is interpreted using SHAP analysis. We show that the DINN captures multi-wave outbreak dynamics across diverse epidemic regions and enables the development of country-specific vector forecasting models. When integrated with the transmission model, the predicted mosquito trajectories support two-year projections of dengue incidence, providing a quantitative framework for early warning and evidence-based control strategies.
